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Record W4399739151 · doi:10.3390/su16125091

Integrating Agricultural Emissions into the European Union Emissions Trading System: Legal Design Considerations

2024· article· en· W4399739151 on OpenAlexaboutno aff
J.M. Verschuuren, Floor Fleurke, Michael C. Leach

Bibliographic record

VenueSustainability · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsGreenhouse gasEmissions tradingEuropean unionAgricultureNatural resource economicsBusinessAgricultural economicsEnvironmental economicsEconomicsEconomic policyGeography

Abstract

fetched live from OpenAlex

In the European Union, greenhouse gas emissions statistics indicate only a slight decreasing trend over the last number of years in emissions from agricultural sources. Unless drastic action is taken in other sectors, the European Union’s 2030 and subsequent climate targets are unlikely to be met without greater reductions made in agricultural emissions. The policy instruments aimed at reducing agricultural emissions that are currently in place have proven to be ineffective; therefore, there is a need to look for new approaches towards bringing agricultural emissions down faster and farther. One obvious new approach is to integrate agricultural emissions into the European Union Emissions Trading System, which, so far, has proven very successful in reducing greenhouse gas emissions in the energy and industrial sectors. Hardly any attention has been paid in the scholarly legal literature to the question of integrating agricultural GHG emissions into emission trading systems. This article seeks to fill this gap. This paper presents the concluding findings of a Dutch Research Council-funded research project that aimed to assess whether and under what conditions the European Union Emissions Trading System could play a role in compelling the agricultural sector to reduce its greenhouse gas emissions. We answered this question by looking at lessons learned from existing examples in the world of market-based approaches to integrating agriculture into emission reduction schemes. To do this, we performed an ex-post assessment of three of the very few examples that exist in the world of such schemes in Canada, California, and Australia, followed by an ex-ante assessment of the prospect of including agricultural emissions under the European Union Emissions Trading System based on the practical experiences of those examples. In the ex-ante study, we evaluated how such inclusion could work, either indirectly, through allowing on-farm offset programs to reward increased carbon sequestration, or directly, by requiring farmers and/or other actors in the agricultural sector to surrender allowances for their direct emissions. As lawyers, we focused mainly on the legal considerations of such a proposition. Having conducted both the ex-ante and ex-post assessments, we conclude that introducing stricter legal instruments of one form or another that will reduce agricultural greenhouse gas emissions and increase carbon removal on agricultural land seems necessary for the European Union if it is serious about achieving its commitments under the Paris Agreement and meeting its obligations under its own Climate Law. The project makes a novel contribution to the legal scholarship in concluding that the most viable starting point for such stricter legislation would be to include methane and nitrous oxide emissions from livestock keeping and synthetic fertilizer use, respectively, under the European Union Emissions Trading System. To start with, this could be conducted by obliging meat and dairy processors and synthetic fertilizer producers to surrender allowances for the on-farm emissions associated with their products. This could be complemented by introducing a voluntary, but still highly regulated, carbon credits scheme that could encourage and reward farmers for reducing their own emissions and for transitioning to net-zero, and overall, more climate-resilient and environmentally friendly farming practices. Such credits could be offered for sale on the private carbon market as well as to Member State governments and the European Commission (through, for example, the Common Agricultural Policy, State Aid schemes, or the Innovation Fund).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.272
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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